Telemedicine for Chronic Heart Failure: An Update

Background: This is a short narrative review of the literature pertaining to telemedicine projects developed in the field of chronic heart failure (CHF), with particular focus on non-invasive telemonitoring projects including the French ones

State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients

Since the beginning of the 1990s, several telemedicine projects and studies focused on type 1 and type 2 diabetes have been developed, including very few elderly diabetic patients. Several of these projects specifically concerned elderly subjects (n = 4). Mainly, these projects and studies show that telemonitoring diabetes results in improved blood glucose control—a significant reduction in HbA1c, improved patient ownership of the disease, greater patient adherence to therapeutic and hygiene-dietary measures, positive impact on comorbidities (hypertension, weight, dyslipidemia), improved quality of life for patients, and at least good patient receptivity and accountability. To date, the magnitude of its effects remains debatable, especially with the variation in patients’ characteristics (e.g., background, ability for self-management, medical condition), sample selection, and approach for treatment of control groups. Over the last 5 years, numerous telemedicine projects based on connected objects and new information and communication technologies (ICT) (elements defining telemedicine 2.0) have emerged or are still under development

Actual as well as Future Technologies and Noninvasive Devices for Optimal Management of Diabetes Mellitus and Chronic Heart Failure

In recent years, several technological innovations have become part of the daily lives of patients suffered from chronic diseases. It is the case for diabetes mellitus and chronic heart failure with noninvasive glucose sensors, intelligent insulin pumps, artificial pancreas, telemedicine, and artificial intelligence for an optimal management. A review of the literature dedicated to these technologies and devices supports the efficacy of the latter. Mainly, these technologies have shown a beneficial effect on diabetes or chronic heart failure management with mainly improvement for these two diseases of patient ownership of the disease; patient adherence to therapeutic and hygiene-dietary measures; the management of comorbidities (hypertension, weight, dyslipidemia); and at least, good patient receptivity and accountability. Especially, the emergence of these technologies in the daily lives of these patients suffered from chronic disease has led to an improvement of the quality of life for patients. Nevertheless, the magnitude of its effects remains to date debatable or to be consolidated, especially with the variation in patients’ characteristics and methods of experimentation and in terms of medical and economic objectives

"IAC++: Un environnement interactif de programmation orientee objet dirigee par une assistance intelligenge pour la reutilisation de composants logiciels"

SIGLEINIST T 73264 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Approche fondée sur la coopération hyper-heuristique pour la planification de chauffeurs de bus

La conception d un système de transport en commun doit prendre en compte différentes dimensions pour résoudre deux problèmes importants d optimisation : l ordonnancement des véhicules (le graphicage) et l affectation des conducteurs (l habillage). Dans nos travaux, nous nous sommes focalisés sur le problème de l'habillage. L objectif est de minimiser le nombre de conducteurs en respectant toutes les contraintes sociales et économiques. Par sa nature combinatoire, l habillage est considéré comme une tâche complexe du processus de conception de réseaux de transport en commun. Nous avons proposé une approche fondée sur les hyper-heuristiques dont le principal avantage réside dans leur faculté d adaptation à différents problèmes. Nous nous sommes intéressés plus particulièrement à une approche coopérative, capable de prendre en compte les changements au cours du processus de résolution. Nous avons étendu les fonctionnalités et amélioré les performances du framework traditionnel des hyper-heuristiques. L algorithme proposé comporte une combinaison de plusieurs phases et plusieurs niveaux. La métaphore de la coalition est utilisée pour permettre la coopération entre hyper-heuristiques. Elle est destinée à favoriser la diversification des solutions et amplifier la capacité de recherche selon un contrôle décentralisé où chaque hyper-heuristique possède une certaine autonomie. Il est ainsi possible d envisager différents modes de coopération entre les hyper-heuristiques : partage de solutions, apprentissage par mimétisme ou encore mise en concurrence de différentes stratégies de recherche. L expérimentation a été réalisée aussi bien sur des instances réelles que sur des benchmarks. Elle a donné de bons résultats tant sur la déviation que sur le temps d exécution.The design of public transport system must take into account different dimensions to solve two main problems of optimization: the vehicles scheduling and driver scheduling. In our work, we focused on bus driver scheduling. Its objective is to minimize the number of drivers in accordance with social and environmental constraints. By its combinatorial nature, bus driver scheduling is considered a complex task in the design process of network transport. We have proposed an approach based on hyper-heuristics whose main advantage lies in their ability to adapt to different problems. We are particularly interested in a cooperative approach, which is able to take into account changes in the resolution process. We have extended the functionality and improved performance of the traditional framework of hyper- heuristics by proposing a pattern based on an organizational model. The proposed algorithm consists of a combination of several phases and several levels. The metaphor of the coalition is used to make cooperate several hyper-heuristics. The coalition is intended to favor diversified solutions and expand search capacity with decentralized control where each hyper-heuristic has certain autonomy. It is thus possible to consider different ways of cooperation between the hyper-heuristics: sharing solutions, learning by mimetism or carrying out different competitive search strategies. The experiment was carried out both on real-world instances and benchmarks. It gave good results on both quality of solution and execution timeBELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF

A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection

An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances

An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques

The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients

A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

Breast cancer is considered one of the most-common types of cancers among females in the world, with a high mortality rate. Medical imaging is still one of the most-reliable tools to detect breast cancer. Unfortunately, manual image detection takes much time. This paper proposes a new deep learning method based on Convolutional Neural Networks (CNNs). Convolutional Neural Networks are widely used for image classification. However, the determination process for accurate hyperparameters and architectures is still a challenging task. In this work, a highly accurate CNN model to detect breast cancer by mammography was developed. The proposed method is based on the Particle Swarm Optimization (PSO) algorithm in order to look for suitable hyperparameters and the architecture for the CNN model. The CNN model using PSO achieved success rates of 98.23% and 97.98% on the DDSM and MIAS datasets, respectively. The experimental results proved that the proposed CNN model gave the best accuracy values in comparison with other studies in the field. As a result, CNN models for mammography classification can now be created automatically. The proposed method can be considered as a powerful technique for breast cancer prediction